A Cloud-Based Machine Learning Approach to Reduce Noise in ECG Arrhythmias for Smart Healthcare Services

Area of Science:

• Biomedical Engineering
• Signal Processing
• Machine Learning

Background:

• Electrocardiograms (ECG) are crucial for cardiac disease detection and monitoring.
• Traditional ECG analysis faces challenges with nonlinear parameters, low signal-to-noise ratios (SNRs), and complex environments.
• Existing methods like extended Kalman filters (EKFs) have limitations in ECG computations.

Purpose of the Study:

• To develop an optimized filter for enhanced ECG signal processing and cardiac disease detection.
• To improve the accuracy and reliability of ECG estimations, especially in noisy conditions.
• To leverage cloud environments for real-time patient updates and disease prediction.

Main Methods:

• Proposed an online filter tracking system with optimization techniques for noise removal.
• Utilized Improved Mutation Chaotic Elephant Herding Optimizations (IMCEHOs) to optimize system nonlinearity.
• Implemented a multi-iterative function (Optimized Iterative UKFs) for predicting unknown target parameters.
• Employed machine learning algorithms for heart disease stage prediction.

Main Results:

• Achieved significant improvements in performance metrics including reduced normalized mean square errors (NMSEs) and root mean squared errors (RMSEs).
• Demonstrated enhanced signal-to-noise ratios (SNRs), reduced variances, and higher accuracies compared to existing methods.
• The proposed method achieved a 91.0% accuracy rate with a 0.05% error rate in noise reduction.

Conclusions:

• The developed optimized filter significantly enhances ECG signal quality and accuracy.
• The system offers a reliable, cloud-based solution for remote patient monitoring and early heart disease detection.
• This approach provides a computationally efficient and derivative-free method for complex signal processing tasks.